Cohesion of Languages in Grammar Networks

Language evolution is a primary problem in designing collaborative agents from a linguistic perspective. The problem of language evolution involves determining how the languages used by a collection of agents may change over time, and whether an initially homogeneous language will stay uniformly understandable or will diverge into mutually unintelligible dialects. In simple regimes, it has been possible to determine properties like the maximum error rate a communication channel can withstand before the agents lose their ability to communicate in a coherent language. This study shows that the evolutionary dynamics of linguistic convergence is affected not only by learning fidelity, but also topology of the population network, language structure, and grammar networks. These factors are just a few that contribute to understanding of complex language convergence dynamics. Four canonical types of graphs with substantially different structural properties were considered in this experiment: the complete graph, a regular ring lattice, a random graph, and a small-world graph. Less-than-complete graphs make sense from a biological or sociological standpoint since learning a language that is similar to preexisting ones would seem easier than learning a drastically different language. For each graph we explored, the dynamics were different. Not only did different networks exhibit different patterns of language convergence, but also reached an equilibrium state at different rates. In technological applications where agents learn from each other, where it is desirable for the overall system to converge, these results may provide some guidance for designing properties of the language or state representation, depending on the degree of convergence desired. c © 2001 John Wiley & Sons, Ltd Cooperative Control of Distributed Multi-Agent Systems Jeff S. Shamma, ed. c © XXXX John Wiley & Sons, Ltd 352 COHESION OF LANGUAGES IN GRAMMAR NETWORKS